1. Technical Field
The present teaching relates generally to method and system of medical imaging and systems incorporating the same. More specifically, the present teaching relates to method and system for 2D/3D data processing in medical imaging and systems incorporating the same.
2. Discussion of Technical Background
In medical imaging, patient data may be acquired in different modalities, such as Computerized Tomography (CT), X-ray, or Magnetic Resonance Imaging (MRI). In today's society, there is a high percentage of the population that suffers from vascular diseases or other related diseases. In diagnosing such diseases, precise recognition and annotation/labeling of proper tubular anatomical structures, such as vascular structures, or airways, play a very important role in making medical diagnosis decisions. Computerized analysis and quantification of vascular structures frequently involves segmentation of vascular structures from other surrounding imaging content, labeling such identified structures, and making accurate measurements of different labeled structures.
Due to noise often present in medical imaging, artifacts, such as partial volume effect in CT images, or imperfection of the segmentation approaches used to identify the vascular structures, the vascular structures segmented from medical images often contain errors. For example, in medical images, vascular structures often appear broken or seemingly contain loops. This makes it difficult to label different vessel structures correctly. In addition, different vascular systems may appear intersecting with each other in images. Such consequences of noisy images make it difficult for a computing device to fully automate the process of efficiently labeling vascular structures. Furthermore, although models for different vascular systems, e.g., an anatomical atlas of human vascular systems, are often available, for the same reason, correctly labeling different segmented vascular structures according to such pre-defined models is difficult.
In an attempt to improve, semi-automated approaches have been developed. For example, solutions exists in which one or more seed points may be manually placed on vessel branches which are then used to automatically label a vessel starting from the seeds. However, this type of approach is usually not reliable because a vessel branch may be incorrectly connected to other vessel branches or to another vascular system because the segmented vessel branches may not be correct due to presence of noise.
Therefore, an improved approach that allows reliable and efficient labeling of vascular structures in inherently noisy medical images is needed.